| NSF Org: |
BCS Division of Behavioral and Cognitive Sciences |
| Recipient: |
|
| Initial Amendment Date: | August 10, 2015 |
| Latest Amendment Date: | October 12, 2016 |
| Award Number: | 1533672 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Kenneth Whang
BCS Division of Behavioral and Cognitive Sciences SBE Directorate for Social, Behavioral and Economic Sciences |
| Start Date: | September 1, 2015 |
| End Date: | August 31, 2020 (Estimated) |
| Total Intended Award Amount: | $868,952.00 |
| Total Awarded Amount to Date: | $868,952.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
5000 FORBES AVE PITTSBURGH PA US 15213-3890 (412)268-8746 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
5000 Forbes Avenue Pittsburgh PA US 15213-2685 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): |
EFRI Research Projects, IntgStrat Undst Neurl&Cogn Sys |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.075 |
ABSTRACT
Movements are inherently variable: one never throws a dart or a basketball in exactly the same way twice. On the face of it, this variability in behavior is detrimental to performance, preventing one from consistently hitting the bull's-eye or making the basket. However, computational theories posit that motor variability may also serve a functional role, enabling exploration and learning of more efficient movements. This creates an intriguing duality: while variability should be minimized for short-term motor performance (to act reliably), it should be maximized for long-term performance (to promote learning). During practice, variability might be useful for developing motor skill. When it's game time, however, variability should be suppressed to the greatest extent possible. Might the central nervous system set the amount of variability in a context-appropriate fashion? This study will investigate the neural correlates of motor variability and establish the connections between neural variability, behavioral performance, and learning.
Neural variability lies at the heart of several theoretical computational models, from implementations of probabilistic computation to Hebbian learning rules. Although the importance of variability has been well recognized, the structure and regulation of neural variability within the central nervous system is not well understood. This project coordinates a program of experiments and new analytical techniques to examine the structure of neural variability in the motor system. It seeks to establish, first, how variability depends on behavioral demands, and second, how variability impacts learning. To achieve this, many neurons of the motor and premotor cortices will be studied simultaneously during performance of demanding behaviors. By studying two distinct areas in the motor pathway, the impacts of noise on motor planning and execution can be examined separately. Furthermore, population recordings can be leveraged to decompose variability into three conceptually distinct components: (1) variability that is related to the task (signal variability), (2) trial-to-trial variability shared among neurons, and (3) private variability within each neuron. The investigators will explore how variability of each type is modulated by task context and learning. These decompositions will yield insight into the mechanisms of variability generation during performance.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Movements are inherently variable: whenever we throw a dart or a basketball, we never do so in the same way twice. On the face of it, this variability in behavior is detrimental to performance, as it prevents us from consistently hitting the bull’s eye or making the basket. However, computational theories posit that motor variability may also serve a functional role, allowing us to explore and learn more efficient movements. This creates an intriguing duality: while variability should be minimized for short-term motor performance (to act reliably), it should be maximized for long-term performance (to promote learning). During practice, variability might be useful for developing motor skill. When it’s game time, however, variability should be suppressed to the greatest extent possible. In this project, we investigated two questions on this theme. (1) Does the central nervous system regulate motor variability in a context-appropriate fashion? (2) Does neural variability regulate learning? Our investigation of these questions uncovered two central new discoveries.
First, we established that motor variability in non-human primates is regulated by reward in the same way that it is for humans: as incentives increase, behavioral performance also increases, but only up to a point. As rewards get too large, performance paradoxically decreases. Our finding is the first to show that animals can “choke under pressure,” just as humans do. By establishing an animal model of this behavior, we can now investigate the neural mechanisms that lead to reward-mediated performance variability.
Second, we discovered that long-term training can fundamentally alter neural variability by enabling the brain to produce new neural activity patterns. These new patterns are causally related to producing new abilities. Our finding lends support for different mechanisms of learning that may regulate neural variability over short- and long-timescales.
In terms of broader impacts, our work has provided training opportunities for 11 separate students and post-doctoral scholars. These trainees all received cross-disciplinary training in neurophysiology, statistics, and machine learning. In addition, we used funds from this grant to host a workshop open to the community on the neuroscience of learning. Finally, this grant helped support the development of new brain-computer interface algorithms that help paralyzed individuals gain control of prosthetic devices through the volitional modulation of neural activity.
Last Modified: 11/30/2020
Modified by: Steven M Chase
Please report errors in award information by writing to: awardsearch@nsf.gov.
